Acquisition and then Separation
Simultaneous sourcing, also called blended sourcing, is an emerging seismic acquisition method for reducing acquisition costs and improving spatial sampling. Conventionally, surveys are acquired by locating a single point source or an array of point sources at a single source location, firing the sources at the same time and then recording the response for the time needed for the sources to finish firing followed by a listening time in which all returns from the subsurface target are recorded. Optionally, the firing of the sources can be repeated and multiple records can be recorded at the same location. Then, the source array is moved to another location, and the process is repeated. The cost of acquiring seismic data by this sequential method is related to the time needed to record each individual source location and the number of such locations, and this cost often limits the ability to record data at fine sampling. By firing one or more point sources at different source locations at the same time or at nearly the same time within the same data record, acquisition time and cost can be reduced and sampling increased. This may be referred to as simultaneous acquisition. Originally, when the method was introduced, the interfering sources were excited at exactly the same time or simultaneously. Today, the same term is also used for acquisition in which sources fire within the same time window as another source even though the firing of the sources is not simultaneous in time and differs by some time delay. Generally, the sources that fire at nearly the same time within the same short record form an extended spatial or areal array, with no expectation that the positions of the individual point sources are close together. The tradeoff with simultaneous acquisition is the need to mitigate the overlapping energy or crosstalk between the sources at different locations by a combination of source encoding in the field and by filtering and source separation techniques in processing. Conventional processing requires individual records for each source location and these must be extracted or separated from the recorded data records.
Simultaneous sourcing is most commonly used for vibroseis sources with long sweep functions, which can be easily encoded. With the vibroseis method, each individual vibrator can be driven by a sweep that differs in some manner from the sweeps for other vibrators within the array, for example using differences in the sweep phase, pseudorandom function, sweep rate, sweep frequency range, start time, etc. Some methods require multiple sweeps and multiple records per location for separation. In the special case that the number of sweeps is greater than or equal to the number of vibrators, then the individual source records can be almost perfectly extracted from the multiple combined records by applying an inverse filter as described for the HFVS method in Sallas, et al. (U.S. Pat. No. 5,721,710). With this and similar methods, it is critical that the sources and the receivers do not move during the multiple sweeps. This method gives high quality separated records, because the separation is well-posed; there are as many input records or sweeps as there are output records or separated seismograms. But because multiple sweeps are needed, the method is not efficient and costs are much higher than single-sweep methods. The tradeoff with doing a single sweep is that the separation is ill-posed, and there will be some residual crosstalk noise after extracting the source seismograms. The cross-talk problem is acerbated by the fact that the vibrators output or signature is imperfectly related to the desired pilot signal by distortion and the addition of harmonics and the actual signal is unknown. The cross talk noise is typically mitigated with an iterative data inversion and separation method (Neelamani, et al., U.S. Pat. No. 8,248,886) or by filtering (Huo et al., U.S. Patent Publication No. 2012/0290214).
Simultaneous sourcing can also be used for impulsive sources but there are fewer and less powerful methods to encode impulsive sources. There is little cost saving benefit for use of simultaneous sourcing for land acquisition with dynamite, but use of simultaneous sourcing for airguns in marine acquisition can be beneficial, especially for wide-azimuth acquisition. The use of random firing times for marine sources firing nearly simultaneously but located on different vessels was disclosed by Vaage (U.S. Pat. No. 6,906,981). More recently, simultaneous sourcing has been proposed for multiple vessel shooting of wide-azimuth (WAZ) marine surveys (Beasley et al., “A 3D simultaneous source field test processed using alternating projections: a new active separation method,” Geophysical Prospecting 60, 591-601 (2012)). Simultaneous sourcing is the only way that finely spaced (e.g. 25-m) source points, can be acquired in a single pass of the streamers. Without simultaneous sourcing, multiple passes are required and the survey takes much longer and costs are significantly higher.
We illustrate one configuration for a WAZ marine survey, in FIG. 1 to show the benefit of simultaneous sourcing. The figure shows source line 123, which is traversed by a source boat, and receiver line 121, which is traversed by a boat pulling multiple streamers of hydrophones. Both boats move in parallel at the same speed, typically a minimum of 6 knots. In the figure, the first position of the boats are shown in black, and future positions are shown in grey. The source boat is fired at position 103 while the receiver boat is at position 101, and the response is recorded typically for about 10 s. During this 10 s, the boats are moving. A few seconds later, the source boat reaches the next shot point at 113, typically 20-40 m from the previous shot, and the receiver boat reaches position 111. To record wider azimuths, the receiver boat can make 4 passes of receiver line 121 while a source boat traverses source lines 123-126 in sequence. This is an expensive option, but can yield a fine source sampling for each source line, for example, a 25-m source interval. Alternatively, 4 source boats can be used, and the sources fired flip-flopping between lines. For example, a source can be fired at position 103, then at position 114, 135, 136, and then 143. If one of the sources fires in flip-flop mode every 25-m, then the source interval along each line (from 103 to 143) is 200 m, much coarser than the fine-spaced survey. It is not possible to shoot and record at a finer shot spacing, because by the time the full record is acquired, the boats have moved tens of meters along the sail lines. A finely spaced survey can be recorded with simultaneous shooting by firing all four sources within the same time record but with a small random delay or jitter in either the firing time or position. For example, the sources can be fired at positions 103, 104, 105, and 106 to form one record with overlapping source energy. Then for the next record, sources are fired at 113, 114, 115, and 116, etc.
The jitter is a form of encoding that allows the interference to be partially removed by filtering in processing. Since the boats are moving, a delay in firing time necessarily means a slight shift in the firing position around the nominal sourcing interval as determined by the speed of the vessel. Instead of requiring vessel-to-vessel time synchronization, it can be operationally simplier to implement random time delays by generating a “preplot” of sourcing positions along each line with random positional variations around the nominal source interval. During acquisition, each vessel shoots independently of the other vessels at the predetermined sourcing positions. With this method, the exact firing position but not the firing time is predetermined, but the result is still randomization in time. In the current invention, the randomization of sourcing time or position is understood to be equivalent. In either case, it is important to determine the actual firing position and firing time and these values along with other sourcing characteristics comprise the encoding function.
The combined data record obtained with simultaneous sourcing must be separated into individual records for each source for conventional processing. A flow-diagram of the standard process is shown in FIG. 2, and the process is illustrated in FIG. 3. In Step 201, source records of length Trecord>Tlisten with multiple source excitations during the record are obtained. Some sort of field-encoding scheme such as jittered start times or position is used during the sourcing. Tlisten is the time needed for the energy to travel from the source to the target and then to the receiver. In FIG. 3, 304 is a simple illustration of a source record for a single-source. In these and subsequent diagrams, the response of a single source is illustrated with a linear event 302 and a hyperbolic event 303. In the simultaneous source record 309, the four sources fire with small time delays and a linear and hyperbolic event from each of the four sources interfere. We assume these 4 sources are at long crossline distances on the source lines 123-126 in FIG. 1, but only one boat and source are shown in the cross-section view of 309. Next in Step 202, the encoding functions, including the source positions and start times, are determined for the sources that contribute to the records. The source location and time projected onto the record window is indicated by the sunburst 301 for the single source. The corresponding source positional variation is relatively small compared to the scale of the figure and is not illustrated in the diagram. The simultaneous source record 309 is generated by sources at projected positions 305, 306, 307, and 308, each having a small time shift relative to each other. Then in Step 203, the encoding function is used to extract individual source records, one for each source starting at the firing time of that source and continuing for the appropriate listening time Tlisten. For vibroseis data, this extraction can include the process of correlation by the particular sweep used for that source. In this marine example, the single record 309 is copied 4 times and then shifted in time so that the record starts (zero time) at the firing time for each respective source. For example, the record 315 is made by copying record 309 and time shifting to the time of source 305. Record 316 corresponds to source 306; record 317 corresponds to 307 and 318 corresponds to 308. This step is sometimes call pseudo-separation or pseudo-deblended. In this example, none of the interference noise has been removed at this stage. Next, in Step 204 further processing methods are used to filter the interfering energy that is not desired on each source gather, or to use a sparse inversion scheme to improve the separation of the data, resulting in a separated seismogram for each source as if it has been recorded independently of the other sources. Then in Step 205, the separated source gathers can be conventionally imaged or inverted.
The same processing method listed in FIG. 2 can also be used for recording on land or on the ocean bottom. With land or ocean-bottom data, it is now possible to record the response of receivers continuously. Wireless receivers contain memory and a clock and can record without stopping for weeks or months. During this time, the sources fire and their firing times are recorded. Multiple sources can be used and to reduce the acquisition time and cost, these source can fire so that the response overlap in time as show in FIG. 4A. FIG. 4A shows a single long continuous record 401 with multiple source excitations illustrated by sunbursts. Unlike the marine streamer case, the sources are not fired at small intervals compared to the record length, and thus conditions are not met for use of the term areal array. But as in the marine streamer case, the initial pseudo-separation Step 203 involves extracting windows the size of the desired record length Tlisten starting at the firing time of one of the sources as shown in FIG. 4B. For example a window corresponding to 412 starting at the source firing time 402 is copied and extracted to make source record 422. It has interfering energy from other sources at 433 and 424. Next a window 413 is copied and extracted for source 403 to make source record 423, and a window 414 is extracted for source 404 making source record 424, and so on.
Simultaneous sourcing followed by source separation can also be used to assist with computationally-expensive seismic data simulation or forward modeling as described in Neelamani et al. (U.S. Pat. No. 8,248,886). Such forward modeling is a component of seismic imaging or seismic inversion with the output being an image of reflectivity or of formation properties such as the seismic velocity of the subsurface. Forward modeling uses a detailed velocity model and computes the complex wavefields theoretically generated by each source. Considerable computer time can be saved by reducing the number of sources to be modeled at one time by using simultaneous sourcing with some sort of encoding scheme, and then separating the data into the individual source seismograms. This method is identical to the field acquisition, but there are more choices of encoding schemes when done in the computer, and the specific encoded-sequence for a source is perfectly known. One common encoding scheme is to use random scaling in which the output of each source is randomly multiplied by either plus or minus one. This scheme cannot be physically implemented in the field for impulse sources such as airguns or explosives.
As described above, simultaneous sourcing can be used to lower costs to acquire seismic data in the field or to simulate seismic data in the computer. This involves recording one or more composite records containing interference from multiple sources. This can be a short record with sources excited close together in time and forming a spatial source array. It also can be continuous long record with individual sources excited at random or fixed intervals. For conventional imaging and inversion, the composite record must be separated into individual source gathers. Typically, this involves pseudo-separation by extracting a window around the firing-time of the sources and then using filtering or inversion operations to remove interference noise or crosstalk. In the special case, that the number of records are the same or greater than the number of individual sources within a spatial array, the separation is quite good, but acquiring multiple records is expensive. With fewer records, there is a problem in that the separation is imperfect with some crosstalk noise remaining or important signal removed by the filtering or inversion.
Inversion without Separation
Simultaneous sourcing is also used to save computational cost associated with imaging and inversion of seismic data. In these methods, individual seismic source gathers that were acquired sequentially, i.e. one source or source array shot at a time, are encoded in the computer and summed to form a simultaneous source record that is then used to form an image of seismic reflectivity or to determine subsurface properties. Use of this method to increase the speed and reduce cost of conventional (non-iterative and does not improve a sub-surface model) migration is disclosed by Ober et al. (U.S. Pat. No. 6,021,094) and use of the method in inversion is disclosed by Krebs, et al. (U.S. Pat. No. 8,121,823). Crosstalk or interference between sources is also a problem for this use of simultaneous sourcing and such crosstalk manifests itself as noise in the imaging and inversion outputs. The crosstalk can be minimized somewhat by optimizing the computer encoding functions, such as using random scaling instead of phase rotation, but the results may not be as good as the more computer-intensive sequential use of individual sources.
Simultaneous sourcing is particularly useful for inversion, such as full waveform inversion (FWI) and least-square reverse-time migration (LSRTM). These methods, unlike traditional imaging, work to iteratively update a trial model to minimize a data misfit function. The model is either subsurface properties such as velocity for FWI, or the reflectivity for LSRTM. Note that the misfit function is computed without source separation. Since both the forward modeling and the model update method are compute intensive, simultaneous sourcing has a large advantage. Typically all the sources in the survey or all the sources in a swath or sail line are encoded and summed to make a very large simultaneous source array. To minimize the crosstalk noise and to improve the results, the sources can be re-encoded and re-summed every iteration and then used for a model update (Krebs, U.S. Pat. No. 8,121,823). Each group of encoded and summed data may be called a realization of the data. The best results and reduced crosstalk are achieved when multiple realizations are used in the iterative process.
A typical process for the use of simultaneous sourcing in inversion is shown in FIG. 5. In Step 501, a number of field records are obtained, each with the same spread extent Lspread and record duration Tlisten. The record duration Tlisten should include the time needed for seismic waves to travel from the source to the target and then to the receivers. A single source or areal array can be used for each record. The records are then computer encoded, preferably with a randomized encoding scheme in Step 502. For example, the records can be randomly multiplied by plus or minus 1 or phase rotated by a random factor. Then all the records in the sail line or swath or in the entire survey are summed or stacked, forming one simultaneous source record. This is called one realization of the data. Then in Step 503, the seismic response is simulated in the computer for traveltime Tlisten for all the sources at one time using the computer encoding scheme. This step uses an initial or updated model. The simulated and measured records are compared in Step 504, and the comparison or misfit function is used to update the subsurface image or property model. If multiple iterations (Step 506) are needed, it is preferable to go back to Step 502 and re-encode the field records, making a second realization of the data. By changing the encoding each iteration, artifacts and residual noise are reduced.
The use of simultaneous-sourcing for iterative inversion assumes that the receiver spread and record length are fixed, i.e. all receivers are recording for all sources for the same length of time so that the records can be summed together. The computer is used to forward-model all the sources into all of the receivers as if they were initiated at the same time or nearly the same time. If the point source data are not recorded with a fixed spread, for example if different receiver locations are used to record different shots, then the forward-modeling case does not match the field data case. This can create problems in that the misfit function, the difference between the field and forward-modeled data, will be dominated by the missing energy between the forward modeling and measured data and will not be useful for updating the trial model. Field data recorded by marine streamer is particularly problematic, in that the receiver steamer moves with the boat and is not fixed. A fixed spread is more commonly achieved on land or ocean-bottom recording, but even in this case a rolling-spread in which the active receiver lines change with source position may be acquired and not meet the assumptions of a fixed spread.
FIG. 6 illustrates the problem with acquiring data conventionally with a marine streamer and then using simultaneous sourcing to reduce the computation effort required in inversion. In 61, a source is fired at position 602 and a record 601 is captured. In this example, the boat then moves forward to position 604 and captures record 603 and then to location 606 for record 605. The receivers are moving so the actual receivers are at different locations along the source line. If all the traces are arranged by their true positions along the sail lines, encoded and summed, a simultaneous source gather 610 is obtained. Then, if the three sources (622, 623, 624, corresponding to 602, 604, and 606) are simultaneously excited in the computer, the simulated record 612 is obtained. There is an immediate mismatch between the measured and simulated data. The measured data do not include traces for receivers to the left of each source, nor do they measure the longer offsets. Thus events shown for the simulated data, for example 644 and 645 and 646, are missing in the measured data record 610. Several methods have been proposed for doing inversion in the case of the non-fixed spread including Rickett, et al. (U.S. Patent Publication No. 2012/0215506), who proposes separating the simulated data before computing the misfit, and Routh et al. (U.S. Patent Publication No. 2012/0143506) who proposes using the cross-correlation objective function. These two solutions are compromises that do not fully solve the problem. There can be errors in separating the data for the first approach, and the cross-correlation objective function is less sensitive to amplitude information in the data compared to the standard least-squares objective function.
Other published attempts to deal with the failure of the fixed-receiver assumption include (1). “Hybrid method for full waveform inversion using simultaneous and sequential source method,” by Routh et al., U.S. Pat. No. 8,437,998; (2) “Simultaneous source encoding and source separation as a practical solution for full wavefield inversion,” by Routh et al., U.S. Publication No. 2012/0073825; (3) “Orthogonal source and receiver encoding,” by Krebs, et al., U.S. Publication No. 2013/0238246; (4) Haber et al., “An effective method for parameter estimation with PDE constraints with multiple right hand sides,” Preprint—UBC at internet address http://www.math.ubc.ca/˜haber/pubs/PdeOptStochV5.pdf (2010).
In this section, we have discussed generating the simultaneous source gather in the computer from data that were recorded sequentially in the field. Krebs, et al. (U.S. Pat. No. 8,121,823) taught that field encoded records that are acquired with an encoded areal source array recorded in a short record could be used in inversion as acquired, without the separation step discussed in the “Acquisition and then Separation” section of this document. By not separating the data, errors from the separation processes are not included in the inversion or imaging steps. Such errors could include a loss or deletion of certain reflection components that are important, for example steep dipping diffractions may be eliminated by error and limit the ability to sharply image bed terminations at small faults. There remains a problem, however, that certain powerful encoding methods available on the computer, such as random scaling, cannot be achieved in the field. In addition, if all the sources are acquired simultaneously in the field with one set of encoding functions, the encoding pattern is fixed and cannot be changed each iteration to make multiple realizations of the data. Finally, the requirements for using simultaneous sourcing for inversion are not always achieved when simultaneous sourcing is used in the field. It is a requirement as discussed above that the data be recorded with a fixed, non-moving spread of receivers for a fixed short length of time. The problems of moving spreads as illustrated above for marine sources is even worse when doing simultaneous sourcing in the field. In addition, it is not practical to use computer simulation to exactly simulate the data as acquired continuously by land wireless receivers for weeks, as illustrated in FIG. 4A. To be practical, the record length simulated in the computer should be short, on the order of a few seconds. No such record with a small group of sources recorded for a period of time while isolated from other sources can be extracted from record 4A.
The present invention uses simultaneous sourcing in the field in such a way as to overcome problems from non-fixed spreads and long recording times to yield a plurality of pseudo super-source records that can be computer encoded and stacked to make multiple realizations of the data that can be changed each iteration of the inversion.